Making room in a data center
Google DeepMind reported that an AlphaEvolve-discovered scheduling heuristic had been in production for more than a year and continuously recovered an average 0.7% of Google's worldwide compute resources.
Original sources ↓ · Revision history ↓
Deployed · source published 2025-05-14
The human problem
Data-center capacity is finite, and idle or poorly allocated compute leaves less capacity for useful work on the same infrastructure.
The prior constraint
A production scheduler must make dependable allocation decisions across changing workloads at very large scale.
AI’s actual role
AlphaEvolve used language models, automated evaluation, and evolutionary search to produce a scheduling heuristic for Google's Borg system.
The documented result
Google says the production heuristic continuously recovers, on average, 0.7% of its worldwide compute resources.
Why it may matter
Recovering capacity can allow more tasks to run on an existing computational footprint.
Limitations
This is a company-reported, Google-specific operational result.
The announcement does not quantify electricity, water, carbon, reliability, or independent-audit outcomes.
Unresolved questions
Would the heuristic transfer to other scheduler designs?
What are the measured energy and reliability effects under representative workloads?
Source history & evidence assessment
- Maturity
- Deployed
- Claim confidence
- unassessed
- Event date
- Not recorded
- Source published
- 2025-05-14
- Captured
- 2026-09-07
- Last source review
- 2026-09-07
- Editorial method
- AI-assisted source review
- Place / relevance
- Not recorded
AI-assisted editorial comparison with the cited primary source; result, setting, source date and limitations retained. Independently checked within the research team. Publication authorized by the site owner; no human source review is claimed.
Maturity describes the tested or operational setting. Confidence describes support for the particular claim; one does not determine the other.
Original sources
AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms ↗ · institution
Institutions: Google DeepMind · Google
Explore the underlying question
Related developments
Editorial connections between distinct settings and results; these links do not imply replication.
Revision & correction history
2026-09-07 · Recovering capacity can allow more tasks to run on an existing computational footprint.
No corrections recorded.